<<<<<<< HEAD
ny_local = 
  read_csv("./data/ny_local.csv")
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   year = col_double(),
##   data_value = col_double(),
##   low_confidence_limit = col_double(),
##   high_confidence_limit = col_double(),
##   population_count = col_double(),
##   city_fips = col_double(),
##   tract_fips = col_double()
=======
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(flexdashboard)
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
ny_local = 
  read_csv("./data/500_Cities.csv") %>% 
  janitor::clean_names() %>% 
  filter(state_abbr == "NY")
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   Year = col_double(),
##   Data_Value = col_double(),
##   Low_Confidence_Limit = col_double(),
##   High_Confidence_Limit = col_double(),
##   PopulationCount = col_double()
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d
## )
## See spec(...) for full column specifications.
covid_symptom_df =
  read_csv("./data/Anxiety_Depression.csv") %>% 
  janitor::clean_names() %>% 
  filter(state == "New York") %>% 
  mutate(
    indicator_id = case_when(
      indicator == "Symptoms of Anxiety Disorder" ~ "Anxiety",
      indicator == "Symptoms of Anxiety Disorder or Depressive Disorder" ~ "Anxiety/Depressive",
      indicator == "Symptoms of Depressive Disorder" ~ "Depressive"
  ))
## Parsed with column specification:
## cols(
##   Phase = col_double(),
##   Indicator = col_character(),
##   Group = col_character(),
##   State = col_character(),
##   Subgroup = col_character(),
##   `Time Period` = col_double(),
##   `Time Period Label` = col_character(),
##   Value = col_double(),
##   `Low CI` = col_double(),
##   `High CI` = col_double(),
##   `Confidence Interval` = col_character(),
##   `Quartile range` = col_character()
## )
mental_care_df =
  read_csv("./data/Mental_Health_Care.csv") %>% 
  janitor::clean_names() %>% 
  filter(state == "New York") %>% 
  mutate(
    indicator_id = case_when(
      indicator == "Needed Counseling or Therapy But Did Not Get It, Last 4 Weeks" ~ "No Therapy",
      indicator == "Received Counseling or Therapy, Last 4 Weeks" ~ "Therapy",
      indicator == "Took Prescription Medication for Mental Health And/Or Received Counseling or Therapy, Last 4 Weeks" ~ "Med/Therapy",
      indicator == "Took Prescription Medication for Mental Health, Last 4 Weeks" ~ "Med"
    )
  )
## Parsed with column specification:
## cols(
##   Indicator = col_character(),
##   Group = col_character(),
##   State = col_character(),
##   Subgroup = col_character(),
##   Phase = col_double(),
##   `Time Period` = col_double(),
##   `Time Period Label` = col_character(),
##   Value = col_double(),
##   LowCI = col_double(),
##   HighCI = col_double(),
##   `Confidence Interval` = col_character(),
##   `Quartile Range` = col_character(),
##   `Suppression Flag` = col_double()
## )
<<<<<<< HEAD =======
chronic_df = 
  read_csv("./data/Chronic_Disease_Indicators.csv") %>% 
  janitor::clean_names()
## Parsed with column specification:
## cols(
##   .default = col_character(),
##   YearStart = col_double(),
##   YearEnd = col_double(),
##   Response = col_logical(),
##   DataValue = col_double(),
##   DataValueAlt = col_double(),
##   LowConfidenceLimit = col_double(),
##   HighConfidenceLimit = col_double(),
##   StratificationCategory2 = col_logical(),
##   Stratification2 = col_logical(),
##   StratificationCategory3 = col_logical(),
##   Stratification3 = col_logical(),
##   ResponseID = col_logical(),
##   StratificationCategoryID2 = col_logical(),
##   StratificationID2 = col_logical(),
##   StratificationCategoryID3 = col_logical(),
##   StratificationID3 = col_logical()
## )
## See spec(...) for full column specifications.
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d
write_csv(ny_local, "./data/ny_local.csv")

Page 1

Column

Chart 1

ub_overall_plot =
  ny_local %>% 
  select(category, measure_id, data_value) %>% 
  filter(category == "Unhealthy Behaviors") %>% 
  ggplot(aes(x = measure_id, y = data_value, color = measure_id)) +
  geom_boxplot() +
  labs(
    x = "Unhealthy Behavior",
    y = "Prevalence (%)"
  ) +
  scale_y_continuous(limits = c(0, 100))

ggplotly(ub_overall_plot)
## Warning: Removed 140 rows containing non-finite values (stat_boxplot).
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Column

Chart 2

mental_health_value =
  ny_local %>%
  select(unique_id, data_value, measure_id, population_count) %>% 
  filter(measure_id == "MHLTH") %>% 
  mutate(value_mental = data_value) %>% 
  select(unique_id, value_mental, population_count)

current_smoking_value =
  ny_local %>%
  select(unique_id, data_value, measure_id, population_count) %>% 
  filter(measure_id == "CSMOKING") %>% 
  mutate(value_smoking = data_value) %>% 
  select(unique_id, value_smoking, population_count)

mental_smoking =
<<<<<<< HEAD
  left_join(mental_health_value, current_smoking_value) %>% 
  drop_na()
======= left_join(mental_health_value, current_smoking_value)
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d
## Joining, by = c("unique_id", "population_count")
current_smoking_plot =
  mental_smoking %>% 
  ggplot(aes(x = value_mental, y = value_smoking, size = population_count)) +
  geom_point(alpha = 0.5, color = "steel blue") +
  labs(
    x = "Mental Health Prevalence (%)",
    y = "Unhealthy Behavior Prevalence (%)",
    title = "Current Smoking"
  ) +
  scale_y_continuous(limits = c(0, 100)) 

ggplotly(current_smoking_plot)
<<<<<<< HEAD
r_smoking = cor(mental_smoking$value_mental, mental_smoking$value_smoking, method = c("pearson"))
r_smoking
## [1] 0.9487768
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Chart 3

binge_drinking_value =
  ny_local %>% 
  select(unique_id, data_value, measure_id) %>% 
  filter(measure_id == "BINGE") %>% 
  mutate(value_binge_drinking = data_value) %>% 
  select(unique_id, value_binge_drinking)

binge_drinking =
  left_join(mental_health_value, binge_drinking_value)
## Joining, by = "unique_id"
binge_drinking_plot =
  binge_drinking %>% 
  ggplot(aes(x = value_mental, y = value_binge_drinking, size = population_count)) +
  geom_point(alpha = 0.5, color = "darkorchid4") +
  labs(
    x = "Mental Health Prevalence (%)",
    y = "Unhealthy Behavior Prevalence (%)",
    title = "Binge Drinking"
  ) +
  scale_y_continuous(limits = c(0, 100))

ggplotly(binge_drinking_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Chart 4

physical_inactivity_value =
  ny_local %>% 
  select(unique_id, data_value, measure_id) %>% 
  filter(measure_id == "LPA") %>% 
  mutate(value_physical_inactive = data_value) %>% 
  select(unique_id, value_physical_inactive)

physical_inactivity =
  left_join(mental_health_value, physical_inactivity_value)
## Joining, by = "unique_id"
physical_inactivity_plot =
  physical_inactivity %>% 
  ggplot(aes(x = value_mental, y = value_physical_inactive, size = population_count)) +
  geom_point(alpha = 0.5, color = "darkcyan") +
  labs(
    x = "Mental Health Prevalence (%)",
    y = "Unhealthy Behavior Prevalence (%)",
    title = "Physical Inactivity"
  ) +
  scale_y_continuous(limits = c(0, 100))

ggplotly(physical_inactivity_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Chart 5

obesity_value =
  ny_local %>% 
  select(unique_id, data_value, measure_id) %>% 
  filter(measure_id == "OBESITY") %>% 
  mutate(value_obesity = data_value) %>% 
  select(unique_id, value_obesity)

obesity =
  left_join(mental_health_value, obesity_value)
## Joining, by = "unique_id"
obesity_plot =
  obesity %>% 
  ggplot(aes(x = value_mental, y = value_obesity, size = population_count)) +
  geom_point(alpha = 0.5, color = "chartreuse4") +
  labs(
    x = "Mental Health Prevalence (%)",
    y = "Unhealthy Behavior Prevalence (%)",
    title = "Obesity"
  ) +
  scale_y_continuous(limits = c(0, 100))

ggplotly(obesity_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Chart 6

sleep_value =
  ny_local %>% 
  select(unique_id, data_value, measure_id) %>% 
  filter(measure_id == "SLEEP") %>% 
  mutate(value_sleep = data_value) %>% 
  select(unique_id, value_sleep)

sleep =
  left_join(mental_health_value, sleep_value)
## Joining, by = "unique_id"
sleep_plot =
  sleep %>% 
  ggplot(aes(x = value_mental, y = value_sleep, size = population_count)) +
  geom_point(alpha = 0.5, color = "darkgoldenrod2") +
  labs(
    x = "Mental Health Prevalence (%)",
    y = "Unhealthy Behavior Prevalence (%)",
    title = "Sleep < 7 hours"
  ) +
  scale_y_continuous(limits = c(0, 100))

ggplotly(sleep_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Page 2

Column

Chart 1

prevention_plot = 
  ny_local %>% 
  filter(category == "Prevention") %>% 
  group_by(measure_id) %>% 
  ggplot(aes(x = measure_id, y = data_value, color = measure_id)) +
  geom_boxplot() +
  labs(
       x = "Prevention", 
       y = "Prevalence (%)") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1))  +
  scale_y_continuous(limits = c(0, 100))

ggplotly(prevention_plot)
## Warning: Removed 226 rows containing non-finite values (stat_boxplot).
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Column

Chart 2

mental_health_df = 
  ny_local %>% 
  select(unique_id, data_value, measure_id, population_count) %>% 
  filter(measure_id == "MHLTH") %>% 
  mutate(value_mental = data_value) %>% 
  select(unique_id, value_mental, population_count)


checkup_df =
  ny_local %>% 
  select(unique_id, data_value, measure_id) %>% 
  filter(measure_id == "CHECKUP") %>% 
  mutate(value_checkup = data_value) %>% 
  select(unique_id, value_checkup)


mental_checkup = 
  left_join(mental_health_df, checkup_df)
## Joining, by = "unique_id"
mental_checkup_plot = 
  mental_checkup %>% 
  ggplot(aes(x = value_mental, y = value_checkup, size = population_count)) +
<<<<<<< HEAD
  geom_point(alpha = 0.5, color = "steel blue") +
=======
  geom_point(color = "steel blue") +
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d
  labs(title = "Current Annual Checkup",
       x = 'Mental Health Prevalence (%)', 
       y = 'Prevention Prevalence (%)') +
  scale_y_continuous(limits = c(0, 100))

ggplotly(mental_checkup_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Chart 3

insurance_df =
  ny_local %>% 
  select(unique_id, data_value, measure_id, population_count) %>% 
  filter(measure_id == "ACCESS2") %>% 
  mutate(value_insurance = data_value) %>% 
  select(unique_id, value_insurance, population_count)

mental_insurance = 
  left_join(mental_health_df, insurance_df)
## Joining, by = c("unique_id", "population_count")
mental_insurance_plot = 
  mental_insurance %>%
  ggplot(aes(x = value_mental, y = value_insurance, size = population_count)) +
<<<<<<< HEAD
  geom_point(alpha = 0.5, color = "chartreuse4") +
=======
  geom_point(color = "chartreuse4") +
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d
  labs(title = "Current Lack of Insurance",
       x = "Mental Health Prevalence (%)", 
       y = "Prevention Prevalence (%)") +
  scale_y_continuous(limits = c(0, 100))

ggplotly(mental_insurance_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Page 3

Row

Chart 1

anxiety_depression_plot =  
  covid_symptom_df %>%
  ggplot(aes(x = time_period, y = value, color = indicator_id)) +
  geom_point() +
  geom_line() +
  scale_x_continuous(
    limits = c(1, 18),
    breaks = seq(1, 18, 1)
  ) +
  labs(
    x = "Time (week)",
    y = "Percentage"
  )

ggplotly(anxiety_depression_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d

Row

Chart 2

mental_care_plot =
  mental_care_df %>% 
  ggplot(aes(x = time_period, y = value, color = indicator_id)) +
  geom_point() +
  geom_line() +
  labs(
    x = "Time (week)",
    y = "Percentage"
  )

ggplotly(mental_care_plot)
<<<<<<< HEAD
=======
>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d
treatment_plot =
  mental_care_df %>% 
  ggplot(aes(x = indicator_id, y = value, color = indicator_id)) +
  geom_boxplot() +
  labs(
    x = "Treatment",
    y = "Percentage"
  )

ggplotly(treatment_plot)
<<<<<<< HEAD
=======
unhealthy_days_mean_plot = 
  chronic_df %>% 
  drop_na(data_value) %>% 
  mutate(year = year_start) %>% 
  filter(question == "Recent mentally unhealthy days among adults aged >= 18 years") %>% 
  group_by(year) %>% 
  summarise(
    mean_of_mentally_unhealthy_days = mean(data_value)
  ) %>% 
  ggplot(aes(x = year, y = mean_of_mentally_unhealthy_days, color = year)) +
  geom_point() +
  geom_line() +
  scale_x_continuous(
    breaks = c(2011,2012,2013,2014,2015,2016,2017,2018)) +
  labs(
    title = "The average mentally unhealthy days for American adults from 2011 to 2018",
    x = "Year",
    y = "The average mentally unhealthy days") 
## `summarise()` ungrouping output (override with `.groups` argument)
unhealthy_days_mean_plot

>>>>>>> c39ca86df3e1516e8c6788dba9336907f1464f4d